Macro scale analysis of Syrah vineyards under winter growing cycles: Agronomical and ecophysiological responses

In Southeast Brazil, the change of grape harvest from wet summer to dry winter through double-pruning management has improved the quality of wines, currently denominated winter wines. In order to better understand the influences of soil, macroclimate, and vineyard management in winter wines, we investigated seven vineyards in the states of Minas Gerais (Três Corações TC, Três Pontas TP, Cordislândia COR, São Sebastião do Paraíso SSP and Andradas AND) and São Paulo (Itobi ITO and Espirito Santo do Pinhal PIN) during three consecutive growing seasons. The vineyards are located in warm temperate zones and grouped in four soil types: Acrudox in TC, AND and SSP, Hapludox in TP; Hapludult in AND and PIN; Eutrudept in ITO. The high clay content (> 35 %) observed in all soil types, associated to low evapotranspiration demand, avoided the occurrence of severe water stress, as observed by the high values of leaf and stem water potential, stomatal conductance, photosynthesis, and transpiration. Differences in vigor were more related to vineyard management and did not affect grape composition. Among vineyards, parameters for berry quality from ITO, such as sugar and acidity, were more associated to high soil sand content and winter temperature. No significant differences were found in anthocyanins and total phenols of berries among vineyards, suggesting that the high thermal range and low precipitation during autumn-winter, historically observed in all municipalities, seemed to be the main factor for improvement of phenolic compounds.(AU)

Macro scale analysis of Syrah vineyards under winter growing cycles: Agronomical and ecophysiological responses

In Southeast Brazil, the change of grape harvest from wet summer to dry winter through double-pruning management has improved the quality of wines, currently denominated winter wines. In order to better understand the influences of soil, macroclimate, and vineyard management in winter wines, we investigated seven vineyards in the states of Minas Gerais (Três Corações TC, Três Pontas TP, Cordislândia COR, São Sebastião do Paraíso SSP and Andradas AND) and São Paulo (Itobi ITO and Espirito Santo do Pinhal PIN) during three consecutive growing seasons. The vineyards are located in warm temperate zones and grouped in four soil types: Acrudox in TC, AND and SSP, Hapludox in TP; Hapludult in AND and PIN; Eutrudept in ITO. The high clay content (> 35 %) observed in all soil types, associated to low evapotranspiration demand, avoided the occurrence of severe water stress, as observed by the high values of leaf and stem water potential, stomatal conductance, photosynthesis, and transpiration. Differences in vigor were more related to vineyard management and did not affect grape composition. Among vineyards, parameters for berry quality from ITO, such as sugar and acidity, were more associated to high soil sand content and winter temperature. No significant differences were found in anthocyanins and total phenols of berries among vineyards, suggesting that the high thermal range and low precipitation during autumn-winter, historically observed in all municipalities, seemed to be the main factor for improvement of phenolic compounds.

Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R2, adjusted R2, root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe2O3 (R2 0.89), TiO2 (R2 0.96), and P2O5 (R2 0.89). Stepwise regressions provided accurate predictions for Al2O3 (R2 0.87) and Ki - molar weathering index (SiO2/Al2O3) (R2 0.74) by incorporating soil texture data, as well as for SiO2 (R2 0.61). Random forest also provided adequate predictions, especially for Fe2O3 (R2 0.95), and improved results of Kr - molar weathering index (SiO2/(Al2O3 + Fe2O3)) (R2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.

Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a “green chemistry” alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R2, adjusted R2, root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe2O3 (R2 0.89), TiO2 (R2 0.96), and P2O5 (R2 0.89). Stepwise regressions provided accurate predictions for Al2O3 (R2 0.87) and Ki - molar weathering index (SiO2/Al2O3) (R2 0.74) by incorporating soil texture data, as well as for SiO2 (R2 0.61). Random forest also provided adequate predictions, especially for Fe2O3 (R2 0.95), and improved results of Kr - molar weathering index (SiO2/(Al2O3 + Fe2O3)) (R2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.(AU)